Maximized Frequency Doubling through the Inverse Design of Nonlinear Metamaterials
Maximized Frequency Doubling through the Inverse Design of Nonlinear Metamaterials
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DOI：
10.1021/acsnano.1c09298
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发表时间：
2022-03-22
期刊：
影响因子：
17.1
通讯作者：
Cai, Wenshan
中科院分区：
文献类型：
--
作者：
Raju, Lakshmi;Lee, Kyu-Tae;Cai, Wenshan
用于开发非线性光学响应的最佳设计的常规过程是基于试错法，该试错法在很大程度上是低效的并且不一定导致理想的结果。深度学习可以使这一过程自动化，并拓宽非线性几何和设备的领域。这项研究说明了一个深度学习框架，用于为非线性超材料创建最佳等离子体设计。该算法产生的等离子体模式，可以最大限度地提高二阶非线性效应的非线性超材料。使用纳米层压超材料作为非线性材料，并在制备的纳米层压材料上制造等离子体图案，以证明深度学习算法的有效性和有效性。产生的最佳图案产生了二次谐波产生与正常入射的基本光的纳米层压。本研究中应用的深度学习架构可以扩展到其他光学响应和光-物质相互作用过程。
The conventional process for developing an optimal design for nonlinear optical responses is based on a trial-and-error approach that is largely inefficient and does not necessarily lead to an ideal result. Deep learning can automate this process and widen the realm of nonlinear geometries and devices. This research illustrates a deep learning framework used to create an optimal plasmonic design for a nonlinear metamaterial. The algorithm produces a plasmonic pattern that can maximize the second-order nonlinear effect of a nonlinear metamaterial. A nanolaminate metamaterial is used as a nonlinear material, and plasmonic patterns are fabricated on the prepared nanolaminate to demonstrate the validity and efficacy of the deep learning algorithm. The optimal pattern produced yielded second-harmonic generation from the nanolaminate with normal incident fundamental light. The deep learning architecture applied in this research can be expanded to other optical responses and light-matter interaction processes.